SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos

Abstract

A hyperspectral video contains frames with numerous spectral bands, providing fine reflectance information for object identification and tracking. Enriched features can be learned from spectral-spatial data using deep learning models. However, due to the difficulty in hyperspectral video collection, deep model training is often insufficient, causing reduced performance during the testing stage. To address this issue, we present a novel Band Attention Grouping-based Siamese framework (SiamBAG) for hyperspectral object tracking. SiamBAG employs massive color object tracking data to train a deep neural network. Band weights obtained by band attention module are used to group a hyperspectral image into multiple three-channel false-color images with approximate total group weights. Then multiple enhanced images obtained by histogram equalization are fed to the proposed SiamBAG network to generate a classification branch, a regression branch and a scale tuning branch. In the classification branch, the response maps of multiple groups are fused by regularized group weights to estimate the position of objects. Then the regression branch is used to obtain the initial object position of objects. The position offsets are fed back to the scale tune branch to relocate and fine-tune the object position by exploiting the similarity between template features and detection features. Experimental results demonstrate that the proposed tracker achieves superior tracking performance than other methods. The source codes of this paper will be released at https://github.com/zephyrhours/Hyperspectral-Object-Tracking-SiamBAG.